class_docstr: Table Question Answering pipeline using a `ModelForTableQuestionAnswering`. This pipeline is only available in
  PyTorch.
  
  This tabular question answering pipeline can currently be loaded from [`pipeline`] using the following task
  identifier: `"table-question-answering"`.
  
  The models that this pipeline can use are models that have been fine-tuned on a tabular question answering task.
  See the up-to-date list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=table-question-answering).
class_docstr_tok: ["Table","Question","Answering","pipeline","using","a","`","ModelForTableQuestionAnswering","`",".","This","pipeline","is","only","available","in","PyTorch",".","This","tabular","question","answering","pipeline","can","currently","be","loaded","from","[","`","pipeline","`","]","using","the","following","task","identifier",":","`","``","table-question-answering","''","`",".","The","models","that","this","pipeline","can","use","are","models","that","have","been","fine-tuned","on","a","tabular","question","answering","task",".","See","the","up-to-date","list","of","available","models","on","[","huggingface.co\/models","]","(","https",":","\/\/huggingface.co\/models","?","filter=table-question-answering",")","."]
code: def __init__(
      self,
      args_parser=TableQuestionAnsweringArgumentHandler(),
      *args,
      **kwargs,
  ):
      super().__init__(*args, **kwargs)
      self._args_parser = args_parser
  
      self.check_model_type(
          dict(
              TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
              + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()
          )
          if self.framework == "tf"
          else dict(
              MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
              + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()
          )
      )
  
      self.aggregate = bool(
          getattr(self.model.config, "aggregation_labels", None)
      ) and bool(
          getattr(self.model.config, "num_aggregation_labels", None)
      )
      self.type = (
          "tapas"
          if hasattr(self.model.config, "aggregation_labels")
          else None
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/table_question_answering.py
imports: import collections
  import types
  import numpy
  from ..utils import add_end_docstrings, is_tensorflow_probability_available, is_tf_available, is_torch_available, requires_backends
  from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException
label_desc: Use image node_id 1 to create a new TableQuestionAnsweringPipeline object from inherited base classes: Pipeline with example: obj = TableQuestionAnsweringPipeline(args_parser)